Real-Time Energy-Efficient Sensor Libraries for Wearable Devices

Abstract

The growing popularity of wearable technology has led to a surge in smartwatch usage among the general public. These devices offer a range of features, including internet connectivity, fitness tracking, and real-time notifications, making them valuable tools for staying connected to the online world while remaining engaged in real-world activities. Smartwatches have become powerful platforms for Human Activity Recognition (HAR) applications thanks to the increasing computational power and the presence of a wide array of sensors, such as accelerometers, gyroscopes, heart rate, and step counters. Efficient real-time data collection from internal sensors is a crucial requirement for HAR applications on wearable devices due to their constraints in battery size and duration. In this paper, we introduce the implementation of three energy-efficient user-level libraries developed for real-time data collection from inertial sensors using native Wear OS APIs and different techniques: Thread, Flow, and Channel. Experiments were conducted on a commercially available Oppo smartwatch comparing them in terms of code size, memory utilization, and energy consumption. The characterization results show that the Channel implementation, which reduces code size by 45% and consumes 75% less energy, is lightweight and versatile. This makes it well-suited for wearable devices without significantly impacting battery life and system performance. Additionally, our findings indicate that choice of programming approach significantly impacts energy consumption, highlighting the importance of optimizing performance and battery life. Furthermore, understanding the interactions between application and system optimization policies is essential for improving energy efficiency in Wear OS applications.

Keywords

• Energy-efficient programming
• wearable devices
• human activity recognition
• wear OS